| Simlex-999: Evaluating semantic models with (genuine) similarity estimation F Hill, R Reichart, A Korhonen Computational Linguistics 41 (4), 665-695, 2015 | 944 | 2015 |
| Glue: A multi-task benchmark and analysis platform for natural language understanding A Wang, A Singh, J Michael, F Hill, O Levy, SR Bowman arXiv preprint arXiv:1804.07461, 2018 | 918 | 2018 |
| The goldilocks principle: Reading children's books with explicit memory representations F Hill, A Bordes, S Chopra, J Weston arXiv preprint arXiv:1511.02301, 2015 | 441 | 2015 |
| The goldilocks principle: Reading children's books with explicit memory representations F Hill, A Bordes, S Chopra, J Weston arXiv preprint arXiv:1511.02301, 2015 | 438 | 2015 |
| Learning distributed representations of sentences from unlabelled data F Hill, K Cho, A Korhonen arXiv preprint arXiv:1602.03483, 2016 | 387 | 2016 |
| Superglue: A stickier benchmark for general-purpose language understanding systems A Wang, Y Pruksachatkun, N Nangia, A Singh, J Michael, F Hill, O Levy, ... Advances in Neural Information Processing Systems, 3266-3280, 2019 | 193 | 2019 |
| Simverb-3500: A large-scale evaluation set of verb similarity D Gerz, I Vulić, F Hill, R Reichart, A Korhonen arXiv preprint arXiv:1608.00869, 2016 | 158 | 2016 |
| Grounded language learning in a simulated 3d world KM Hermann, F Hill, S Green, F Wang, R Faulkner, H Soyer, D Szepesvari, ... arXiv preprint arXiv:1706.06551, 2017 | 135* | 2017 |
| Specializing word embeddings for similarity or relatedness D Kiela, F Hill, S Clark Proceedings of the 2015 Conference on Empirical Methods in Natural Language …, 2015 | 129 | 2015 |
| Learning to understand phrases by embedding the dictionary F Hill, KH Cho, A Korhonen, Y Bengio Transactions of the Association for Computational Linguistics 4, 17-30, 2016 | 117 | 2016 |
| Neural arithmetic logic units A Trask, F Hill, SE Reed, J Rae, C Dyer, P Blunsom Advances in Neural Information Processing Systems, 8035-8044, 2018 | 84 | 2018 |
| Measuring abstract reasoning in neural networks A Santoro, F Hill, D Barrett, A Morcos, T Lillicrap International Conference on Machine Learning, 4477-4486, 2018 | 81* | 2018 |
| Learning abstract concept embeddings from multi-modal data: Since you probably can’t see what I mean F Hill, A Korhonen Proceedings of the 2014 Conference on Empirical Methods in Natural Language …, 2014 | 75 | 2014 |
| Improving multi-modal representations using image dispersion: Why less is sometimes more D Kiela, F Hill, A Korhonen, S Clark Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 73 | 2014 |
| Hyperlex: A large-scale evaluation of graded lexical entailment I Vulić, D Gerz, D Kiela, F Hill, A Korhonen Computational Linguistics 43 (4), 781-835, 2017 | 65 | 2017 |
| Learning to understand goal specifications by modelling reward D Bahdanau, F Hill, J Leike, E Hughes, A Hosseini, P Kohli, ... arXiv preprint arXiv:1806.01946, 2018 | 58* | 2018 |
| Analysing mathematical reasoning abilities of neural models D Saxton, E Grefenstette, F Hill, P Kohli arXiv preprint arXiv:1904.01557, 2019 | 54 | 2019 |
| The goldilocks principle F Hill, A Bordes, S Chopra, J Weston arXiv preprint arXiv:1511.02301, 2015 | 53 | 2015 |
| Not all neural embeddings are born equal F Hill, KH Cho, S Jean, C Devin, Y Bengio arXiv preprint arXiv:1410.0718, 2014 | 48 | 2014 |
| Embedding word similarity with neural machine translation F Hill, K Cho, S Jean, C Devin, Y Bengio arXiv preprint arXiv:1412.6448, 2014 | 46 | 2014 |
Anna KorhonenProfessor, Computer Science and Linguistics, University of CambridgeVerified email at cam.ac.uk
